Presentation
About things

By Kelvin Chilvers-Jones

Contents

  • Content 1
  • Tables
  • Visualisations

Content 1

  • Some text
  • Some more text
  • R code
# Define a server for the Shiny app
function(input, output) {
  
  # Fill in the spot we created for a plot
  output$graph <- renderPlot({
    # Render a barplot
  })
}
  • Python code, with code highlighting
import numpy as np
import matplotlib.pyplot as plt

r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r
fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})
ax.plot(theta, r)
ax.set_rticks([0.5, 1, 1.5, 2])
ax.grid(True)
plt.show()

Tables

Ways of displaying tables

Transition Description
none No transition (default, switch instantly)
fade Cross fade
slide Slide horizontally
convex Slide at a convex angle
concave Slide at a concave angle
zoom Scale the incoming slide so it grows in from the center of the screen.
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

Visualisations

Make clear, easy to understand visualisations with annotations as needed

Code for determining p-value

library(dplyr)
library(ggplot2)

# ice cream sales data
sales <- c(5,5,10,5,15,20,35,40,35,20,10,5)
type <- c("not summer", "not summer", "not summer", "not summer", "summer", "summer", "summer", "summer", "summer", "not summer", "not summer", "not summer")
months <- c("Jan-23","Feb-23","Mar-23","Apr-23","May-23","Jun-23","Jul-23","Aug-23","Sep-23","Oct-23","Nov-23","Dec-23")

# make df
df_ices <- data.frame(sales=sales, months=months, type=type)

summer_months <- c("May-23","Jun-23","Jul-23","Aug-23", "Sep-23")

# get sum of sales for summer
sum_sales <- df_ices %>%
  filter(months %in% summer_months)

# get sum of sales for not summer
not_sum_sales <- df_ices %>%
  filter(!(months %in% summer_months))

# compare numbers and get p-value
sum_sales <- sum(sum_sales$sales)
not_sum_sales <- sum(not_sum_sales$sales)


# Hypothesis to test: sales are higher in the summer

proa<-prop.test(not_sum_sales,sum_sales, p=0.5, correct = FALSE)
#proa
proa <- list(proa)
proa <- as.data.frame(matrix(unlist(proa),nrow=length(proa),byrow=TRUE))
colnames(proa) <- c("X-squared","df","p-value","prop 1","prop 2","95pct conf int1","95pct conf int2","hypothesis","test name","vectors")

########## graph the p-value ##########################
graph <- rbind(proa$`p-value`)
graph <- as.data.frame(as.numeric(graph))
colnames(graph) <- c("p-value")
graph$xaxis <- c("Summer compared to not summer")

display<- ggplot(graph,aes(x=xaxis,y=`p-value`))+
  geom_bar(aes(fill=xaxis),position="dodge",stat="identity")+
  geom_text(aes(label=round(`p-value`,2)),vjust=-1.0,size=3)+
  scale_fill_manual(values=alpha(c("#CD2456","#14022E","#CD1234")))+
  #ylim(0,max(graph$`p-value`)+1)+
  theme_classic(base_size=10)+
  theme(legend.position = "none")+
  labs(x="AB Test",y="P-value",title="P-value for AB test")

p-value of < 0.05 means accept H1 alternative hypothesis of “sales increase in summer”

Questions

Any questions?